Isolating the Roles of Religion, Ethnicity, and Political Ideology in Mass Atrocities, 1800–2020
Bibliographic record
Abstract
Religion, ethnicity, and political ideology all lend themselves to the perpetration of mass atrocities by creating a sense of identity that sets up an Us/Them dichotomy. Atrocities are modelled here as arising from the motive of acquiring territory but augmented by other-regarding preferences that capture the role of identity. My empirical results using data for the period 1800–2020 confirm that all these identity-driven motivators are associated with mass atrocities, with religion being more powerful than ethnicity. Monotheistic religions (with the strong exception of Judaism) are seen to be associated with more mass atrocity deaths than (polytheistic) Hinduism, lending partial credibility to Hume’s (1757/2010) view on the intolerance of monotheism. While democracies are associated with fewer mass atrocities than autocracies, Christian liberal democracies are not. My statistical analysis rejects the popular presumption that Islam is more violent than Christianity. In fact, in the post-World War II (WWII) era, among the major religions Christianity has been associated with the most mass atrocity deaths. The results also show that mass deaths were higher in atrocities that took place in settler colonies, especially in the post-WWII period of decolonisation. Using mass atrocities as the metric of violence, the correlations found in the empirical work of this paper offer many new and surprising findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".